✓ Dynamic ad insertion in live streams
✓ Clean ad breaks without workflow disruption
✓ Smooth playback across all devices
✓ Stream-ready transcoding with SRT passthrough
#adinsertion #livestreaming #monetization #5centsCDN
✓ Dynamic ad insertion in live streams
✓ Clean ad breaks without workflow disruption
✓ Smooth playback across all devices
✓ Stream-ready transcoding with SRT passthrough
#adinsertion #livestreaming #monetization #5centsCDN
Cheap recipe to reproduce 3G + low-CPU playback bugs locally:
• Use a network shaper (or Android/Network Link Conditioner)
• Throttle CPU with desktop devtools or low-power device profiles
• Record a 30s screen+nettrace for repro
Cheap recipe to reproduce 3G + low-CPU playback bugs locally:
• Use a network shaper (or Android/Network Link Conditioner)
• Throttle CPU with desktop devtools or low-power device profiles
• Record a 30s screen+nettrace for repro
Cold-cache prewarm: synthetic plays vs origin-priming with range requests — which to pick?
• Synthetic plays simulate real cache patterns (ABR, ranges)
• Range-request priming is cheaper but may miss ABR tails
• Hybrid: tiny synthetic plays for hot ranges + range priming for the rest
Cold-cache prewarm: synthetic plays vs origin-priming with range requests — which to pick?
• Synthetic plays simulate real cache patterns (ABR, ranges)
• Range-request priming is cheaper but may miss ABR tails
• Hybrid: tiny synthetic plays for hot ranges + range priming for the rest
• <3s → better latency, higher request rate and origin pressure
• 4–6s → balanced for most CDNs and mobile
• >8–10s → fewer requests, worse seek/latency
Start at 4s for general use; tune per-region cache behavior. ⚖️📡
• <3s → better latency, higher request rate and origin pressure
• 4–6s → balanced for most CDNs and mobile
• >8–10s → fewer requests, worse seek/latency
Start at 4s for general use; tune per-region cache behavior. ⚖️📡
Avoid slow sidecar fetches: bundle critical audio/subtitle track pointers and small inline cues into the manifest so the player can avoid extra roundtrips for first-play.
• Inline ids & offsets for mandatory captions
• Fall back to sidecars for non-critical extras
Avoid slow sidecar fetches: bundle critical audio/subtitle track pointers and small inline cues into the manifest so the player can avoid extra roundtrips for first-play.
• Inline ids & offsets for mandatory captions
• Fall back to sidecars for non-critical extras
• Package short segments that contain only keyframes for fast seek points
• Keep them tiny and cache-friendly on the same CDN path
• Use for fast ad splice or live jump-to-live
Trade a bit of storage for near-instant seeks. 🔁🔍
• Package short segments that contain only keyframes for fast seek points
• Keep them tiny and cache-friendly on the same CDN path
• Use for fast ad splice or live jump-to-live
Trade a bit of storage for near-instant seeks. 🔁🔍
Idea: ABR hints at the edge. Let edge compute return a tiny region/profile hint (bandwidth-tier, common-isp-tail) so the player can bias startup bitrate without extra RTTs.
• Keeps decision local & fast
• Helps in noisy mobile ISPs
• Combine with privacy-safe aggregates only
Idea: ABR hints at the edge. Let edge compute return a tiny region/profile hint (bandwidth-tier, common-isp-tail) so the player can bias startup bitrate without extra RTTs.
• Keeps decision local & fast
• Helps in noisy mobile ISPs
• Combine with privacy-safe aggregates only
Infra worked. CDN worked. DNS worked.
The app failed.
That’s the difference between availability and experience.
If users see nothing, the product is down—no matter how green your dashboards look.
Design for graceful failure, not perfection.
Infra worked. CDN worked. DNS worked.
The app failed.
That’s the difference between availability and experience.
If users see nothing, the product is down—no matter how green your dashboards look.
Design for graceful failure, not perfection.
Instrument player startup like web perf: add named marks (manifest-parsed, init-fetched, first-frame) and capture spans client-side. Benefits:
• Pinpoint where wait happens (DNS, init, chunk)
• Correlate with device CPU & network
• Use low-sample-rate high-res traces for regressions
Instrument player startup like web perf: add named marks (manifest-parsed, init-fetched, first-frame) and capture spans client-side. Benefits:
• Pinpoint where wait happens (DNS, init, chunk)
• Correlate with device CPU & network
• Use low-sample-rate high-res traces for regressions
• Drop frame-rate before resolution for talk/headless content
• Drop resolution first for high-motion scenes
• Too many switches → CPU & battery spikes
Make a simple heuristic: scene-type + battery hint → one prioritized knob. 🎯📱
• Drop frame-rate before resolution for talk/headless content
• Drop resolution first for high-motion scenes
• Too many switches → CPU & battery spikes
Make a simple heuristic: scene-type + battery hint → one prioritized knob. 🎯📱
Micro-manifests: send a tiny manifest first (init + first-seg URL) and follow with the full playlist. Why it helps:
• Browser/client parses and starts requests earlier
• Reduces TTFF by prioritizing critical bytes
• Works great with HTTP/3 prioritization
Micro-manifests: send a tiny manifest first (init + first-seg URL) and follow with the full playlist. Why it helps:
• Browser/client parses and starts requests earlier
• Reduces TTFF by prioritizing critical bytes
• Works great with HTTP/3 prioritization
ABR changes can hide as battery regressions. Quick test plan:
• Run repeatable playback on a low-end device with power logging
• Compare energy-per-minute for ABR configs (sustained bitrate vs switches)
• Prefer fewer costly switches if they pull more power than they save bandwidth
ABR changes can hide as battery regressions. Quick test plan:
• Run repeatable playback on a low-end device with power logging
• Compare energy-per-minute for ABR configs (sustained bitrate vs switches)
• Prefer fewer costly switches if they pull more power than they save bandwidth
• Hash+salt identifiers at edge and drop raw IPs (truncate)
• Aggregate windows before exporting (minute buckets)
• Keep per-session traces only on error samples
Privacy-first design keeps legal happy and analytics usable. 🛡️📊
• Hash+salt identifiers at edge and drop raw IPs (truncate)
• Aggregate windows before exporting (minute buckets)
• Keep per-session traces only on error samples
Privacy-first design keeps legal happy and analytics usable. 🛡️📊
Don't transcode everything upfront—transcode the hot parts first.
Pipeline idea:
• Generate a cheap low-res proxy immediately
• Predict popularity (views, timestamps) or wait for first accesses
• Promote hot segments/files to full renditions on-demand or scheduled
Don't transcode everything upfront—transcode the hot parts first.
Pipeline idea:
• Generate a cheap low-res proxy immediately
• Predict popularity (views, timestamps) or wait for first accesses
• Promote hot segments/files to full renditions on-demand or scheduled
Make scrubbing feel instant: create tiny low-res I-frame thumbnails every N seconds and store them with the manifest.
Best practices:
• Use fixed interval I-frames (e.g., every 2–5s)
• Serve from same CDN endpoint to avoid cross-origin delays
• Keep preview files tiny and lazy-loaded
Make scrubbing feel instant: create tiny low-res I-frame thumbnails every N seconds and store them with the manifest.
Best practices:
• Use fixed interval I-frames (e.g., every 2–5s)
• Serve from same CDN endpoint to avoid cross-origin delays
• Keep preview files tiny and lazy-loaded
• 1% region/device → 10% → 50%
• Tie rollout cohorts to QoE gates (startup, rebuffer)
• Auto-rollback on KPI regressions
Less drama, faster iterations. Rollouts are tests—treat them like experiments. 🧪🚦
• 1% region/device → 10% → 50%
• Tie rollout cohorts to QoE gates (startup, rebuffer)
• Auto-rollback on KPI regressions
Less drama, faster iterations. Rollouts are tests—treat them like experiments. 🧪🚦
ABR should respect user & business signals. Ideas to reduce cost without wrecking UX:
• Expose app-level hint (data_saver, premium) to the ABR logic
• Let server clamp top bitrate regionally during peak egress
• Use quick telemetry to revert clamping if QoE drops
ABR should respect user & business signals. Ideas to reduce cost without wrecking UX:
• Expose app-level hint (data_saver, premium) to the ABR logic
• Let server clamp top bitrate regionally during peak egress
• Use quick telemetry to revert clamping if QoE drops
Need to swap live sources or insert ads with zero glitch? Quick checklist:
• Align GOP/keyframes across feeds
• Signal splice points with SCTE or timed metadata
• Keep segment boundaries consistent (1–2s low-latency)
• Warm edge caches for the target segment range
Need to swap live sources or insert ads with zero glitch? Quick checklist:
• Align GOP/keyframes across feeds
• Signal splice points with SCTE or timed metadata
• Keep segment boundaries consistent (1–2s low-latency)
• Warm edge caches for the target segment range
Perceived startup beats raw metrics. Serve a tiny 16–32px poster/first-frame from edge as an instant visual placeholder while the first chunk arrives.
Why it works:
• Instant visual feedback → fewer abandonments
• Cheap to store & cache
Perceived startup beats raw metrics. Serve a tiny 16–32px poster/first-frame from edge as an instant visual placeholder while the first chunk arrives.
Why it works:
• Instant visual feedback → fewer abandonments
• Cheap to store & cache
• Put auth in an Authorization header (CDN caches on path only)
• Or normalize cache-key at edge (strip signature) and validate via signed cookie
Either way: separate auth from cache-key. Saves egress and surprises. 🧩🔐
• Put auth in an Authorization header (CDN caches on path only)
• Or normalize cache-key at edge (strip signature) and validate via signed cookie
Either way: separate auth from cache-key. Saves egress and surprises. 🧩🔐
Telemetry is noisy—send less, catch more.
Quick pattern I use:
• Reservoir-sample baseline sessions (2–5%)
• Always keep high-res traces for error sessions & tail cohorts
• Aggregate histograms at the edge (reduce payloads, keep percentiles)
Telemetry is noisy—send less, catch more.
Quick pattern I use:
• Reservoir-sample baseline sessions (2–5%)
• Always keep high-res traces for error sessions & tail cohorts
• Aggregate histograms at the edge (reduce payloads, keep percentiles)